We propose a novel system for extracting multiple people in crowded scenes by employing LED 3D range sensor. This new kind of device can capture depth image at a relatively high frame speed, which makes the extraction of dynamic objects possible. However, it suffers from the strong noise caused by the environment illumination. Here we propose a novel method for multiple people extraction by integrating an improved version of mean-shift clustering algorithm and total variation based denoising technique. When handling the depth image, it is considered both as a point cloud as well as a 2D image. In this way, different properties of the depth image are sufficiently exploited. The proposed method can fully automatically extract multiple people in a cluttered environment, even from a mobile platform. The experiment conducted at the platform of a railway station demonstrates the effectiveness of our proposed algorithm.

We propose a novel system for tracking a variable number of pedestrians in crowded scenes by exploiting laser range scanners. Based on the specific pattern generated by walking feet in the spatio-temporal domain, a walking model is constructed and applied to track pedestrians. To track interactive targets, an algorithm based on Interactive Multiple Particle Filters (IMPF) is proposed, whose computation load increases linearly with the number of targets. To handle a variable number of pedestrians, spatio-temporal correlation analysis in combination with a mean shift based clustering technique is proposed. Compared with camera-based surveillance methods, our system provides a novel technique for automatically tracking a large number of pedestrians in a relatively large area. The experiments, in which over 2600 pedestrians were tracked in 10 minutes at a 60 m × 20 m subway station, show the effectiveness of our proposed algorithm.

In this research, we proposed a system of detecting and monitoring pedestrians' motion trajectories at a populated and wide environment, such as exhibition hall, supermarket etc., using the horizontally profiling single-row laser range scanners on a mobile platform. A simplified walking model is defined to track the rhythmic swing feet at the ground level. Pedestrians are recognized by detecting the braided styles, which is a typical appearance that could discriminate the data of moving feet with other mobile and motionless objects. Two experiments are conducted. One is at the laboratory environment, the purpose of which is to examine the algorithm in details. Another is at an exhibition hall, a populated and wide environment, the purpose is to examine whether the system could be applied for practical needs. It is a big challenge, while the system did well. Pedestrians in the exhibition hall at the moment of measurement are detected. Their motion trajectories are extracted, and associated to the background map, which is made of the motionless objects, and covers the whole exhibition hall.

We propose a novel system for tracking multiple pedestrians in a crowded scene by exploiting single-row laser range scanners that measure distances of surrounding objects. A walking model is built to describe the periodicity of the movement of the feet in the spatial-temporal domain, and a mean-shift clustering technique in combination with spatial- temporal correlation analysis is applied to detect pedestrians. Based on the walking model, particle filter is employed to track multiple pedestrians. Compared with camera-based methods, our system provides a novel technique to track multiple pedestrians in a relatively large area. The experiments, in which over 300 pedestrians were tracked in 5 minutes, show the validity of the proposed system.